{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pytorch 支持 sklearn 的重参数搜索需要其他的库。一般的项目中设置好命令行脚本后，可以通过更改脚本参数手动实现超参数搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=8, releaselevel='final', serial=0)\n",
      "matplotlib 3.6.3\n",
      "numpy 1.23.5\n",
      "pandas 1.5.2\n",
      "sklearn 1.2.0\n",
      "torch 1.13.1+cu117\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 20640\n",
      "\n",
      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      "    :Attribute Information:\n",
      "        - MedInc        median income in block group\n",
      "        - HouseAge      median house age in block group\n",
      "        - AveRooms      average number of rooms per household\n",
      "        - AveBedrms     average number of bedrooms per household\n",
      "        - Population    block group population\n",
      "        - AveOccup      average number of household members\n",
      "        - Latitude      block group latitude\n",
      "        - Longitude     block group longitude\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
      "\n",
      "The target variable is the median house value for California districts,\n",
      "expressed in hundreds of thousands of dollars ($100,000).\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "An household is a group of people residing within a home. Since the average\n",
      "number of rooms and bedrooms in this dataset are provided per household, these\n",
      "columns may take surpinsingly large values for block groups with few households\n",
      "and many empty houses, such as vacation resorts.\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n",
      "(20640, 8)\n",
      "(20640,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "print(housing.DESCR)\n",
    "print(housing.data.shape)\n",
    "print(housing.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,\n",
      "         1.02380952e+00,  3.22000000e+02,  2.55555556e+00,\n",
      "         3.78800000e+01, -1.22230000e+02],\n",
      "       [ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,\n",
      "         9.71880492e-01,  2.40100000e+03,  2.10984183e+00,\n",
      "         3.78600000e+01, -1.22220000e+02],\n",
      "       [ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,\n",
      "         1.07344633e+00,  4.96000000e+02,  2.80225989e+00,\n",
      "         3.78500000e+01, -1.22240000e+02],\n",
      "       [ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,\n",
      "         1.07305936e+00,  5.58000000e+02,  2.54794521e+00,\n",
      "         3.78500000e+01, -1.22250000e+02],\n",
      "       [ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,\n",
      "         1.08108108e+00,  5.65000000e+02,  2.18146718e+00,\n",
      "         3.78500000e+01, -1.22250000e+02]])\n",
      "--------------------------------------------------\n",
      "array([4.526, 3.585, 3.521, 3.413, 3.422])\n"
     ]
    }
   ],
   "source": [
    "# print(housing.data[0:5])\n",
    "import pprint  #打印的格式比较 好看\n",
    "\n",
    "pprint.pprint(housing.data[0:5])\n",
    "print('-'*50)\n",
    "pprint.pprint(housing.target[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11610, 8) (11610,)\n",
      "(3870, 8) (3870,)\n",
      "(5160, 8) (5160,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#拆分训练集和测试集，random_state是随机种子,同样的随机数种子，是为了得到同样的随机值\n",
    "x_train_all, x_test, y_train_all, y_test = train_test_split(\n",
    "    housing.data, housing.target, random_state = 7)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "    x_train_all, y_train_all, random_state = 11)\n",
    "# 训练集\n",
    "print(x_train.shape, y_train.shape)\n",
    "# 验证集\n",
    "print(x_valid.shape, y_valid.shape)\n",
    "# 测试集\n",
    "print(x_test.shape, y_test.shape)\n",
    "\n",
    "dataset_maps = {\n",
    "    \"train\": [x_train, y_train],\n",
    "    \"valid\": [x_valid, y_valid],\n",
    "    \"test\": [x_test, y_test],\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "\n",
    "class HousingDataset(Dataset):\n",
    "    def __init__(self, mode='train'):\n",
    "        self.x, self.y = dataset_maps[mode]\n",
    "        self.x = torch.from_numpy(scaler.transform(self.x)).float()\n",
    "        self.y = torch.from_numpy(self.y).float().reshape(-1, 1)\n",
    "            \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        return self.x[idx], self.y[idx]\n",
    "    \n",
    "    \n",
    "train_ds = HousingDataset(\"train\")\n",
    "valid_ds = HousingDataset(\"valid\")\n",
    "test_ds = HousingDataset(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.2981,  0.3523, -0.1092, -0.2506, -0.0341, -0.0060,  1.0806, -1.0611]),\n",
       " tensor([1.5140]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#回归模型我们只需要1个数\n",
    "\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self, input_dim=8):\n",
    "        super().__init__()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(input_dim, 30),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(30, 1)\n",
    "            )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x.shape [batch size, 8]\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        # logits.shape [batch size, 1]\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        \n",
    "    return np.mean(loss_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    " \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "\n",
    "                    # 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    for idx, item in enumerate(train_df.columns):\n",
    "        plt.plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        plt.plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        plt.grid()\n",
    "        plt.legend()\n",
    "        # plt.xticks(range(0, train_df.index[-1], 10*sample_step), range(0, train_df.index[-1], 10*sample_step))\n",
    "        plt.xlabel(\"step\")\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "# plot_learning_curves(record)  #横坐标是 steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39a62e64b10845dda9ffe268e65f6b20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4600 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.01\n"
     ]
    },
    {
     "data": {
      "image/png": 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     "text": [
      "loss:     0.3822\n"
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   "source": [
    "for lr in [1e-2, 3e-2, 3e-1, 1e-3]:\n",
    "\n",
    "    epoch = 100\n",
    "\n",
    "    model = NeuralNetwork()\n",
    "\n",
    "    # 1. 定义损失函数 采用MSE损失\n",
    "    loss_fct = nn.MSELoss()\n",
    "    # 2. 定义优化器 采用SGD\n",
    "    # Optimizers specified in the torch.optim package\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)\n",
    "\n",
    "    # 3. early stop\n",
    "    early_stop_callback = EarlyStopCallback(patience=10, min_delta=1e-3)\n",
    "\n",
    "    model = model.to(device)\n",
    "    record = training(\n",
    "        model, \n",
    "        train_loader, \n",
    "        val_loader, \n",
    "        epoch, \n",
    "        loss_fct, \n",
    "        optimizer, \n",
    "        early_stop_callback=early_stop_callback,\n",
    "        eval_step=len(train_loader)\n",
    "        )\n",
    "    print(\"lr: {}\".format(lr))\n",
    "    plot_learning_curves(record)\n",
    "    model.eval()\n",
    "    loss = evaluating(model, val_loader, loss_fct)\n",
    "    print(f\"loss:     {loss:.4f}\")"
   ]
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